Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?
2026-08-31 • Computation and Language
Computation and Language
AI summaryⓘ
The authors created a game called DoppelBot to see how well middle school students can tell when an AI is pretending to be a person. They studied how playing the game helped kids notice clues beyond just language, like social hints, to spot the AI. Over time, students got better at recognizing AI and thought about important ideas like privacy and what AI can or can’t do. The researchers also shared the game conversations and how students voted to help others study this too.
Large Language ModelsAI impersonationsocial deduction gamesprivacy awarenessmiddle school studentsAI detectioncontextual reasoningdata privacyhuman-AI interactionpersonalization
Authors
Dan Schumacher, Pragathi Durga Rajarajan, Haven Kotara, Roman Rendon, Kosi Atupulazi, Deepti Tagare, Ismaila Temitayo Sanusi, Fred G. Martin, Anthony Rios
Abstract
LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially important for adolescents, who use generative AI frequently but may struggle to recognize it. We introduce \textit{DoppelBot}, a cooperative social deduction game designed to study how young people detect and respond to AI impersonation. Through studies with middle schoolers, we investigate whether a DoppelBot prompts reflection on privacy and impersonation, how repeated exposure affects AI-detection accuracy as agents become more personalized, and which strategies students use to identify AI doppelgängers. We find that students' detection accuracy improves over time, driven by a shift from relying on linguistic cues to leveraging shared social and contextual signals. Students also demonstrated an understanding of AI limitations such as embodiment and reflected on broader issues such as data privacy. To support future research, we release an anonymized dataset of game transcripts and voting behavior.